Customized Pricing Recommender System Simple Implementation and Preliminary Experiments

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1 THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE., ,,, Customized Pricing Recommender System Simple Implementation and Preliminary Experiments Toshihiro KAMISHIMA, Shotaro AKAHO, and Jun SAKUMA, National Institute of Advanced Industrial Science and Technology (AIST) AIST Tsukuba Central 2, Umezono 1 1 1, Tsukuba, Ibaraki, Japan Graduate School of SIE, University of Tsukuba Tennodai, Tsukuba, Ibaraki, Japan Japan Scienece and Technology Agency 5 3, Yonban-cho, Chiyoda-ku, Tokyo, Japan Abstract Recommender systems suggests items that would be preferred to customers. Here, we propose to add new function, price discounting, to these systems. This new system determines whether it offers discounting for each customer, and thus this price-setting scheme is called price customization. We discuss the benefits that this customized pricing recommender system will bring for both customers and dealers. We propose to realize such systems by combining standard recommendation algorithm and multi-armed bandit approaches. We implemented a simple system and performed preliminary experiments on semi-simulated data. Key words recommender system, collaborative filtering, price customization, multi-armed bandit 1. (recommender system) [1][3]GroupLens [4] 90 [3] Bergemann Ozmen 1

2 [5] [6] [7] (price customization) [8], [9] (dynamic pricing) (price personalization) 1 Dahana [8] 1CNN.com - Web sites change prices based on customers habits: 2

3 1 α β γ 1 B A A A B B A B (A B) A (CPRS; Customized Pricing Recommender System) CPRS CPRS CPRS CPRS CPRS 2 α β β < α 1 (standard) 2 (discount) 3 (indifferent) α β 0 γ

4 3 CPRS 2 2 (class imbalance problem) [7] 2 1 α β - (exploit-explore) (multi-armed bandit) [10], [11] softmax UCB1Gittins plsa

5 - 3 CPRS 2 CPRS Movielens plsa % 1.4% αβ γ F Grouplens research lab: 2 plsa % ɛ plsa Web [12][14]Web Web Weng [15] 5

6 Shani [6] Baydar [16] 6. (CPRS) CPRS CPRS CPRS CPRS CPRS (attendant system) CPRS [1] (1)(3),, Vol. 22, No. 6 Vol. 23, No. 2 ( ). [2] P. Resnick and H. R. Varian: Recommender systems, Communications of The ACM, 40, 3, pp (1997). [3] J. Ben Schafer, J. A. Konstan and J. Riedl: E-commerce recommendation applications, Data Mining and Knowledge Discovery, 5, pp (2001). [4] P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom and J. Riedl: GroupLens: An open architecture for collaborative filtering of Netnews, Proc. of The Conf. on Computer Supported Cooperative Work, pp (1994). [5] D. Bergemann and D. Ozmen: Optimal pricing with recommender system, ACM Conference on Electronic Commerce, pp (2006). [6] G. Shani, D. Heckerman and R. I. Brafman: An mdpbased recommender system, Journal of Machine Learning Research, 6, pp (2005). [7] N. Japkowicz: Learning from imbalanced data sets: A comparison of various strategies, AAAI Workhop: Learning from Imbalanced Data Sets, pp (2000). [8] N. Terui and W. D. Dahana: Price customization using price thresholds estimated from scanner panel data, Journal of Interactive Marketing, 20, pp (2006). [9],, 37, 2, pp (2008). [10] R. S. Sutton and A. G. Barto:, (2000).. [11] P. Auer, N. Cesa-Bianchi and P. Fischer: Finite-time analysis of the multiarmed bandit problem, Machine Learning, 47, pp (2002). [12] N. Abe and A. Nakamura: Learning to optimally schedule internet banner advertisements, Proc. of The 16th Int l Conf. on Machine Learning, pp (1999). [13] D. Agarwal, B.-C. Chen and P. Elango: Explore/exploit schemes for web content optimization, Proc. of The 9th IEEE Int l Conf. on Data Mining, pp (2009). [14] L. Li, W. Chu, J. Langford and R. E. Shapire: A contextual-bandit approach to personalized news article recommendation, Proc. of The 19th Int l Conf. on World Wide Web, pp (2010). [15] L.-T. Weng, Y. Xu, Y. Li and R. Nayak: Towards information enrichment through recommendation sharing, Data mining and Multi-agent Integration (Ed. by L. Cao), Springer, chapter 7, pp (2009). [16] C. Baydar: Agent-based modeling and simulation of store performance for personalized pricing, proc. of the Winter Simulation Conf. (2003). [17] T. Hofmann and J. Puzicha: Latent class models for collaborative filtering, Proc. of the 16th Int l Joint Conf. on Artificial Intelligence, pp (1999). [18] A. Das, M. Datar, A. Garg and S. Rajaram: Google news personalization: Scalable online collaborative filtering, Proc. of The 16th Int l Conf. on World Wide Web, pp (2007). [19] R. M. Bell and Y. Koren: Scalable collaborative filtering with jointly derived neighborhood interpolation weights, Proc. of The 7th IEEE Int l Conf. on Data Mining, pp (2007). [20] Y. Koren: Collaborative filtering with temporal dynamics, Proc. of The 15th Int l Conf. on Knowledge Discovery and Data Mining, pp (2009). 1. (collaborative filtering; CF) ; active user ; sample user CF x {1,..., n} y {1,..., m} x y s xy 5 D = {(x k, y k, s xk y k )}, k = 1,..., N 6

7 x y ŝ xy = f(x, y) D x Y x = {y (x, y, s) D s.t. x = x} plsa plsa CF [17]Google News [18] [17] 3 z {1,..., K} K x y z s z D L(D; Θ) = X log X Pr[z] Pr[x z] Pr[y z]n (s; µ z, σz) 2 z (x,y,s) D loss(d; Θ) = X (s xy ŝ xy ) 2 + λr (x,y,s) D R L 2 λ (A 1) EM x y P z ŝ xy = E[s x, y] = µz Pr[z] Pr[x z] Pr[y z]n (s; 0, σ2 z) Pr[z] Pr[x z] Pr[y z] P z K = [19] D b s xy = s xy b y d y s xy = s xy d y x s xy c x s xy = s xy c x D s xy plsa ŝ xy ŝ xy = ŝ xy + b + c x + d y [20] (4) ŝ xy = b + c x + d y + u y h v x + 1 p Yx X y Y x w y i (A 1) bc x d y u y v x w y K K = 5 u y v x w y 7

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